Trustworthy AI in Healthcare: Explainable Federated Learning for Medical
Keywords:
Artificial Intelligence (AI), Ethical AI, Explainable AI (XAI), Federated Learning (FL), Lung disease detection, Medical diagnosis, Patient trust in AI, Privacy-preserving healthcareAbstract
Artificial Intelligence (AI) has revolutionized the healthcare industry, enabling innovations such as early disease detection, personalized treatment, and improved patient outcomes. However, despite its potential, AI adoption in healthcare faces significant barriers, including concerns about data privacy, algorithmic transparency, and the trustworthiness of AI-driven decisions. Federated Learning (FL), combined with Explainable AI (XAI), offers a solution to these challenges by enabling decentralized model training and providing human-understandable explanations for AI predictions. Federated Learning (FL) is a distributed machine learning approach that allows data to remain on local devices or within hospital systems, thus preserving patient privacy while allowing for model improvements across multiple sites. This enables collaboration without the need to share sensitive data, addressing privacy concerns in healthcare settings. However, for clinicians to trust AI-generated recommendations, these models need to be interpretable, leading to the integration of Explainable AI (XAI) techniques such as Grad-CAM, SHAP, and LIME. These techniques help generate insights into how AI models make decisions, making them more transparent and actionable in clinical contexts. This chapter examines the synergy between FL and XAI, highlighting their potential to create trustworthy AI systems that are both privacy-preserving and clinically interpretable. We explore several real-world and research-driven use cases, including early detection of lung diseases, diabetic retinopathy classification, cancer diagnosis, cardiovascular risk prediction, sepsis forecasting, and neurodegenerative disease detection. In each case, FL enables data collaboration without data centralization, while XAI ensures that the decision-making process is understandable to healthcare professionals, thereby fostering clinical trust and adoption.
Additionally, the chapter addresses ethical considerations surrounding the use of AI in healthcare, such as informed consent, data ownership, algorithmic bias, and regulatory compliance. We also explore future directions for developing robust and ethical AI systems in healthcare, focusing on improving model generalizability, fairness, and inclusivity. By integrating FL with XAI, healthcare providers can leverage the full potential of AI while ensuring the security, transparency, and accountability required to make AI-driven decisions both trusted and effective.
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